Over/Under Predictions - NCAA Football 2014 Win Totals

When I started writing this piece, I thought it would be useful to support my picks with some sort of objective analysis to separate it from just another opinion. I therefore decided to create a regression model which factored in, what I considered to be, numerous predictors of wins and losses for a team in a given season. The results of the model were by no means overwhelming but it did correctly pick the over/under for 44 out of 70 teams for 2013 (63%). So, there is some value in it – just not enough to completely neglect qualitative analysis and blindly wager my next paycheque on the output. I'll also point out at this stage that the odds-makers no doubt have even more sophisticated models but they'll often adjust their lines based on a team's popularity and historical betting tendencies. For example, a popular team's predicted wins will be inflated as more casual bettors will “buy” or cheer these teams on. For that reason, this type of objective analysis can be more accurate over several years and with a large enough sample.